Recommender systems tutorial.pdf

7/25/2019 Recommender systems tutorial.pdf

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Tutorial:Recommender

Systems

InternationalJointConferenceonArtificialIntelligence

Beijing,August4,2013

DietmarJannach

TUDortmund

Gerhard

FriedrichAlpenAdriaUniversittKlagenfurt


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- 2 - Dietmar Jannach, Markus Zanker and Gerhard Friedrich


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RecommenderSystems

Applicationareas


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IntheSocialWeb


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Evenmore

Personalizedsearch

"Computationaladvertising"


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Aboutthespeakers

GerhardFriedrich

ProfessoratUniversityKlagenfurt,Austria

DietmarJannach

ProfessoratTUDortmund,Germany

Researchbackgroundandinterests

ApplicationofIntelligentSystemstechnologyinbusiness

Recommendersystemsimplementation&evaluation

Productconfigurationsystems

Webmining

Operationsresearch


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Agenda

Whatarerecommendersystemsfor?

Introduction

Howdo

they

work

(Part

I)

?

CollaborativeFiltering

Howtomeasuretheirsuccess?

Evaluationtechniques

Howdo

they

work

(Part

II)

?

ContentbasedFiltering

KnowledgeBasedRecommendations

HybridizationStrategies

Advancedtopics

Explanations

Humandecisionmaking


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WhyusingRecommenderSystems?

Valueforthecustomer

Findthingsthatareinteresting

Narrowdownthesetofchoices

Helpmeexplorethespaceofoptions

Discovernewthings

Entertainment

Valuefortheprovider

Additionalandprobablyuniquepersonalizedserviceforthecustomer

Increasetrustandcustomerloyalty

Increasesales,clicktroughrates,conversionetc. Opportunitiesforpromotion,persuasion

Obtainmoreknowledgeaboutcustomers


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Realworldcheck

Mythsfromindustry

Amazon.comgeneratesXpercentoftheirsalesthroughtherecommendation

lists(30


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Problemdomain

Recommendationsystems(RS)helptomatchuserswithitems

Easeinformationoverload

Salesassistance(guidance,advisory,persuasion,)

RSaresoftwareagentsthatelicittheinterestsandpreferencesofindividual

consumers[]andmakerecommendationsaccordingly.

Theyhave

the

potential

to

support

and

improve

the

quality

of

the

decisionsconsumersmakewhilesearchingforandselectingproductsonline.

[Xiao&Benbasat,MISQ, 2007]

Differentsystem

designs

/paradigms

Basedonavailabilityofexploitabledata

Implicitandexplicituserfeedback

Domaincharacteristics


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Recommendersystems

RSseenasafunction[AT05]

Given: Usermodel(e.g.ratings,preferences,demographics,situationalcontext)

Items(withorwithoutdescriptionofitemcharacteristics)

Find:

Relevancescore.Usedforranking.

Finally:

Recommenditemsthatareassumedtoberelevant

But: Rememberthatrelevancemightbecontextdependent

Characteristicsofthelistitselfmightbeimportant(diversity)


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Paradigmsofrecommendersystems

Recommendersystemsreduce

informationoverloadbyestimating

relevance


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Personalizedrecommendations


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Collaborative:"Tellmewhat'spopular

amongmypeers"


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Contentbased:"Showmemoreofthe

samewhatI'veliked"


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Knowledgebased:"Tellmewhatfits

basedonmyneeds"


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Hybrid:combinationsofvariousinputs

and/orcompositionofdifferent

mechanism


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Recommendersystems:basictechniques

Pros Cons

Collaborative Noknowledge

engineeringeffort,

serendipityofresults,

learnsmarketsegments

Requiressomeformofrating

feedback,coldstartfornewusers

andnewitems

Contentbased Nocommunityrequired,

comparisonbetweenitemspossible

Contentdescriptionsnecessary,

coldstartfornewusers,nosurprises

Knowledgebased Deterministic

recommendations,

assuredquality,nocoldstart,canresemblesales

dialogue

Knowledgeengineeringeffortto

bootstrap,basicallystatic,does

notreacttoshorttermtrends


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CollaborativeFiltering(CF)

Themostprominentapproachtogeneraterecommendations

usedbylarge,commercialecommercesites

wellunderstood,variousalgorithmsandvariationsexist

applicableinmanydomains(book,movies,DVDs,..)

Approach

use

the

"wisdom

of

the

crowd"

to

recommend

items Basicassumptionandidea

Usersgiveratingstocatalogitems(implicitlyorexplicitly)

Customerswhohadsimilartastesinthepast,willhavesimilartastesinthe

future


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1992: Usingcollaborativefilteringtoweaveaninformationtapestry,D.Goldbergetal.,CommunicationsoftheACM

Basicidea:"Eagerreadersreadalldocsimmediately,casualreaderswait

fortheeagerreaderstoannotate"

ExperimentalmailsystematXeroxParcthatrecordsreactionsofusers

whenreadingamail

Usersareprovidedwithpersonalizedmailinglistfiltersinsteadofbeing

forcedto

subscribe

Contentbasedfilters(topics,from/to/subject)

Collaborativefilters

E.g.Mailsto[all]whichwererepliedby[JohnDoe]andwhichreceived

positiveratings

from

[X]

and

[Y].


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1994: GroupLens:anopenarchitectureforcollaborativefilteringofnetnews,P.Resnicketal.,ACMCSCW

Tapestrysystemdoesnotaggregateratingsandrequiresknowingeach

other

Basicidea:"Peoplewhoagreedintheirsubjectiveevaluationsinthe

pastarelikelytoagreeagaininthefuture"

Buildsonnewsgroupbrowserswithratingfunctionality


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Userbasednearestneighborcollaborativefiltering(1)

Thebasictechnique:

Givenan"activeuser"(Alice)andanitemInotyetseenbyAlice

ThegoalistoestimateAlice'sratingforthisitem,e.g.,by

findasetofusers(peers)wholikedthesameitemsasAliceinthepastand

whohaverateditemI

use,e.g.theaverageoftheirratingstopredict,ifAlicewilllikeitemI

dothisforallitemsAlicehasnotseenandrecommendthebestrated

Item1 Item2 Item3 Item4 Item5

Alice 5 3 4 4 ?

User1 3 1 2 3 3User2 4 3 4 3 5

User3 3 3 1 5 4

User4 1 5 5 2 1


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Userbasednearestneighborcollaborativefiltering(2)

Somefirstquestions

Howdowemeasuresimilarity?

Howmanyneighborsshouldweconsider?

Howdowegenerateapredictionfromtheneighbors'ratings?


Alice 5 3 4 4 ?

User1 3 1 2 3 3

User2 4 3 4 3 5

User3 3 3 1 5 4

User4 1 5 5 2 1


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Measuringusersimilarity

ApopularsimilaritymeasureinuserbasedCF:Pearsoncorrelation

a,b :usersra,p :ratingofuseraforitemp

P :setofitems,ratedbothbyaandb

Possiblesimilarityvaluesbetween 1and1; =user'saverageratings


Alice 5 3 4 4 ?

User1 3 1 2 3 3

User2 4 3 4 3 5

User3 3 3 1 5 4

User4 1 5 5 2 1

sim =0,85sim =0,70

sim =

0,79

,


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Pearsoncorrelation

Takesdifferencesinratingbehaviorintoaccount

Workswellinusualdomains,comparedwithalternativemeasures

suchascosinesimilarity

0

1

2

3

4

5

6

Item1 Item2 Item3 Item4

Ratings

Alice

User1

User4


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Makingpredictions

Acommonpredictionfunction:

Calculate,

whether

the

neighbors'

ratings

for

the

unseen

itemiare

higher

orlowerthantheiraverage

Combinetheratingdifferences usethesimilarityasaweight

Add/subtractthe neighbors'biasfromtheactiveuser'saverageanduse

thisasaprediction


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Makingrecommendations

Makingpredictionsistypicallynottheultimategoal

Usualapproach(inacademia)

Rankitemsbasedontheirpredictedratings

However

Thismightleadtotheinclusionof(only)nicheitems

Inpractice

also:Takeitempopularityintoaccount

Approaches

"Learningtorank"

Optimizeaccordingtoagivenrankevaluationmetric(seelater)


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Improvingthemetrics /predictionfunction

Notallneighborratingsmightbeequally"valuable"

Agreementoncommonlylikeditemsisnotsoinformativeasagreementon

controversialitems Possiblesolution: Givemoreweighttoitemsthathaveahighervariance

Valueofnumberofcorateditems

Use"significanceweighting",bye.g.,linearlyreducingtheweightwhenthe

numberofcorateditemsislow

Caseamplification

Intuition:Givemoreweightto"verysimilar"neighbors,i.e.,wherethe

similarityvalueiscloseto1.

Neighborhoodselection

Usesimilaritythresholdorfixednumberofneighbors


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Memorybasedandmodelbasedapproaches

UserbasedCFissaidtobe"memorybased"

theratingmatrixisdirectlyusedtofindneighbors/makepredictions

doesnotscaleformostrealworldscenarios

largeecommercesiteshavetensofmillionsofcustomersandmillionsof

items

Modelbasedapproaches

basedonanofflinepreprocessingor"modellearning"phase

atruntime,onlythelearnedmodelisusedtomakepredictions

modelsareupdated/retrainedperiodically

largevarietyoftechniquesused

modelbuildingandupdatingcanbecomputationallyexpensive


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2001: Itembasedcollaborativefilteringrecommendationalgorithms,B.

Sarwaret

al.,

WWW

2001

ScalabilityissuesarisewithU2Uifmanymoreusersthanitems

(m>>n,m=|users|,n=|items|)

e.g.Amazon.com SpacecomplexityO(m2)whenprecomputed

TimecomplexityforcomputingPearsonO(m2n)

Highsparsityleadstofewcommonratingsbetweentwousers

Basicidea:"ItembasedCFexploitsrelationshipsbetweenitemsfirst,

insteadof

relationships

between

users"


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Itembasedcollaborativefiltering

Basicidea:

Usethesimilaritybetweenitems(andnotusers)tomakepredictions

Example:

LookforitemsthataresimilartoItem5

TakeAlice'sratingsfortheseitemstopredicttheratingforItem5


Alice 5 3 4 4 ?

User1 3 1 2 3 3

User2 4 3 4 3 5

User3 3 3 1 5 4

User4 1 5 5 2 1


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Thecosinesimilaritymeasure

Producesbetterresultsinitemtoitemfiltering

forsomedatasets,noconsistentpictureinliterature

Ratingsareseenasvectorinndimensionalspace

Similarityiscalculatedbasedontheanglebetweenthevectors

Adjustedcosinesimilarity

takeaverageuserratingsintoaccount,transformtheoriginalratings

U:setofuserswhohaveratedbothitemsaandb


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Preprocessingforitembasedfiltering

Itembasedfilteringdoesnotsolvethescalabilityproblemitself

PreprocessingapproachbyAmazon.com(in2003)

Calculateallpairwiseitemsimilaritiesinadvance

Theneighborhoodtobeusedatruntimeistypicallyrathersmall,because

onlyitemsaretakenintoaccountwhichtheuserhasrated

Itemsimilaritiesaresupposedtobemorestablethanusersimilarities

Memoryrequirements

UptoN2 pairwisesimilaritiestobememorized(N=numberofitems)in

theory

Inpractice,thisissignificantlylower(itemswithnocoratings)

Furtherreductionspossible

Minimumthresholdforcoratings(items,whichareratedatleastbynusers)

Limitthesizeoftheneighborhood(mightaffectrecommendationaccuracy)


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Moreonratings

PureCFbasedsystemsonlyrelyontheratingmatrix

Explicitratings

Mostcommonlyused(1to5,1to7Likert responsescales)

Researchtopics

"Optimal"granularityofscale;indicationthat10pointscaleisbetteracceptedin

moviedomain

Multidimensionalratings(multipleratingspermovie)

Challenge

Usersnotalwayswillingtoratemanyitems;sparseratingmatrices

Howtostimulateuserstoratemoreitems?

Implicitratings

clicks,pageviews,timespentonsomepage,demodownloads

Canbeusedinadditiontoexplicitones;questionofcorrectnessofinterpretation


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Datasparsityproblems

Coldstartproblem

Howtorecommendnewitems?Whattorecommendtonewusers?

Straightforwardapproaches

Ask/forceuserstorateasetofitems

Useanothermethod(e.g.,contentbased,demographicorsimplynon

personalized)intheinitialphase

Alternatives

Usebetteralgorithms(beyondnearestneighborapproaches)

Example:

Innearestneighborapproaches,thesetofsufficientlysimilarneighborsmight

betosmalltomakegoodpredictions

Assume"transitivity"ofneighborhoods


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Examplealgorithmsforsparsedatasets

RecursiveCF

Assumethereisaverycloseneighbornofuwhohoweverhasnotratedthe

targetitemiyet. Idea:

ApplyCFmethodrecursivelyandpredictaratingforitemifortheneighbor

Usethispredictedratinginsteadoftheratingofamoredistantdirect

neighbor


Alice 5 3 4 4 ?

User1 3 1 2 3 ?User2 4 3 4 3 5

User3 3 3 1 5 4

User4 1 5 5 2 1

sim =0,85

Predict

ratingfor

User1


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Graphbasedmethods

"Spreadingactivation"(sketch)

Idea:Usepathsoflengths>3

torecommenditems Length3:RecommendItem3toUser1

Length5:Item1alsorecommendable


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Moremodelbasedapproaches

Plethoraofdifferenttechniquesproposedinthelastyears,e.g.,

Matrixfactorizationtechniques,statistics

singularvaluedecomposition,principalcomponentanalysis

Associationrulemining

compare:shoppingbasketanalysis

Probabilisticmodels

clusteringmodels,Bayesiannetworks,probabilisticLatentSemanticAnalysis Variousothermachinelearningapproaches

Costsofpreprocessing

Usuallynotdiscussed

Incrementalupdatespossible?


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2000: ApplicationofDimensionalityReductionin

Recommender

System,

B.

Sarwar

et

al.,

WebKDD

Workshop

Basicidea:Trademorecomplexofflinemodelbuildingforfasteronline

predictiongeneration

SingularValue

Decomposition

for

dimensionality

reduction

of

rating

matrices

Capturesimportantfactors/aspectsandtheirweightsinthedata

factorscanbegenre,actorsbutalsononunderstandableones

Assumptionthatkdimensionscapturethesignalsandfilteroutnoise(K=20to100)

Constanttimetomakerecommendations

ApproachalsopopularinIR(LatentSemanticIndexing),data

compression,


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Apicturesays

-1

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1

BobMary

Alice

Sue


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Matrixfactorization

VkT

Dim1 0.44 0.57 0.06 0.38 0.57

Dim2 0.58 0.66 0.26 0.18 0.36

Uk Dim1 Dim2

Alice 0.47 0.30

Bob 0.44 0.23

Mary 0.70 0.06

Sue 0.31 0.93 Dim1 Dim2

Dim1 5.63 0

Dim2 0 3.23

T

kkkk

VUM

k

SVD:

Prediction:

=3+0.84=3.84

)()( EPLVAliceUrr Tkkkuui


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Associationrulemining

Commonlyusedforshoppingbehavioranalysis

aimsatdetectionofrulessuchas

"Ifacustomer

purchases

baby

food

then

he

also

buys

diapers

in70%ofthecases"

Associationruleminingalgorithms

candetectrulesoftheformX=>Y(e.g.,babyfood=>diapers)fromasetof

salestransactionsD={t1,t2,tn}

measureofquality:support,confidence


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Probabilisticmethods

Basicidea(simplisticversionforillustration):

giventheuser/itemratingmatrix

determinetheprobabilitythatuserAlicewilllikeanitemi basetherecommendationonsuchtheseprobabilities

CalculationofratingprobabilitiesbasedonBayes Theorem

Howprobableisratingvalue"1"forItem5givenAlice'spreviousratings?

CorrespondstoconditionalprobabilityP(Item5=1|X),where

X=Alice'spreviousratings=(Item1=1,Item2=3,Item3=)

CanbeestimatedbasedonBayes'Theorem

Usually

more

sophisticated

methods

used Clustering

pLSA


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2008: Factorizationmeetstheneighborhood:amultifacetedcollaborativefilteringmodel,Y.Koren,ACMSIGKDD

StimulatedbyworkonNetflixcompetition

Prizeof$1,000,000foraccuracyimprovementof10%RMSE

comparedto

own

Cinematch system

Verylargedataset(~100Mratings,~480Kusers,~18K

movies)

Lastratings/userwithheld(setK)

Rootmean

squared

error

metric

optimized

to

0.8567

K

rr

RMSE Kiu

uiui

),(

2)(


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Mergesneighborhoodmodelswithlatentfactormodels

Latentfactormodels

goodto

capture

weak

signals

in

the

overall

data

Neighborhoodmodels

goodatdetectingstrongrelationshipsbetweencloseitems

Combinationinonepredictionsinglefunction

Localsearchmethodsuchasstochasticgradientdescenttodetermine

parameters

Addpenaltyforhighvaluestoavoidoverfitting

2008: Factorizationmeetstheneighborhood:amultifacetedcollaborativefilteringmodel,Y.Koren,ACMSIGKDD

Kiu

iuiui

T

uiuuibqp

bbqpqpbbr),(

22222

,,)()(min

***

i

T

uiuui qpbbr


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Summarizingrecentmethods

Recommendationisconcernedwithlearningfromnoisyobservations

(x,y),where

hastobedeterminedsuch that

isminimal.

Avarietyofdifferentlearningstrategieshavebeenappliedtryingto

estimatef(x)

Nonparametricneighborhoodmodels

MFmodels,SVMs,NeuralNetworks,BayesianNetworks,

yxf )(

y

yy

2)(


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CollaborativeFilteringIssues

Pros:

wellunderstood,workswellinsomedomains,noknowledgeengineeringrequired

Cons: requiresusercommunity,sparsityproblems,nointegrationofotherknowledgesources,

noexplanationofresults

WhatisthebestCFmethod?

Inwhichsituationandwhichdomain?Inconsistentfindings;alwaysthesamedomains

anddatasets;differencesbetweenmethodsareoftenverysmall(1/100)

Howtoevaluatethepredictionquality?

MAE/RMSE:WhatdoesanMAEof0.7actuallymean?

Serendipity:Notyetfullyunderstood

Whataboutmultidimensionalratings?


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OneRecommenderSystemsresearchquestion

Whatshouldbeinthatlist?

RecommenderSystemsineCommerce


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Anotherquestionbothinresearchandpractice

Howdoweknowthatthesearegood

recommendations?



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Dietmar Jannach, Markus Zanker and Gerhard Friedrich

Thismightleadto

Whatisagoodrecommendation?

Whatisagoodrecommendationstrategy?

Whatisagoodrecommendationstrategyformy

business?


We hope you will buy also These have been in stock for quite a while now


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- 54 -


Totalsalesnumbers

Promotionofcertainitems

Clickthroughrates

Interactivityonplatform

Customerreturnrates

Customersatisfactionandloyalty

Whatisagoodrecommendation?

Whatarethemeasuresinpractice?


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Purposeandsuccesscriteria(1)

Differentperspectives/aspects

Dependsondomainandpurpose

No

holistic

evaluation

scenario

exists

Retrievalperspective

Reducesearchcosts

Provide"correct"proposals Assumption:Usersknowinadvancewhattheywant

Recommendationperspective

Serendipity identifyitemsfromtheLongTail Usersdidnotknowaboutexistence


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WhendoesaRSdoitsjobwell?

"Recommendwidely

unknownitemsthat

usersmightactually

like!"

20%ofitems

accumulate74%ofallpositiveratings

Recommenditemsfromthelongtail

d i i ( )


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Purposeandsuccesscriteria(2)

Predictionperspective

Predicttowhatdegreeuserslikeanitem

Most

popular

evaluation

scenario

in

research

Interactionperspective

Giveusersa"goodfeeling"

Educate

users

about

the

product

domain Convince/persuadeusers explain

Finally,conversionperspective

Commercialsituations Increase"hit","clickthrough","lookerstobookers"rates

Optimizesalesmarginsandprofit

Howdoweasresearchers


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Testwithrealusers

A/Btests

Examplemeasures:salesincrease,clickthroughrates

Laboratorystudies

Controlledexperiments

Examplemeasures: satisfactionwiththesystem(questionnaires)

Offlineexperiments

Basedonhistoricaldata

Examplemeasures:predictionaccuracy,coverage

know?

E i i l h


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Empiricalresearch

Characterizingdimensions:

Whoisthesubjectthatisinthefocusofresearch?

Whatresearchmethodsareapplied?

Inwhichsettingdoestheresearchtakeplace?

Subject Onlinecustomers,students,historical onlinesessions,computers,

Researchmethod Experiments,quasiexperiments, nonexperimental

research

Setting Lab,realworld scenarios

Research methods


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Researchmethods

Experimentalvs.nonexperimental(observational)researchmethods

Experiment(test,trial):

"Anexperiment

is

astudy

in

which

at

least

one

variable

is

manipulated

and

unitsarerandomlyassignedtodifferentlevelsorcategoriesofmanipulated

variable(s)."

Units:users,historicsessions,

Manipulatedvariable:typeofRS,groupsofrecommendeditems,

explanationstrategies

Categoriesofmanipulatedvariable(s):contentbasedRS,collaborativeRS

Experiment designs


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Experimentdesigns

Evaluation in information retrieval (IR)


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Evaluationininformationretrieval(IR)

Recommendationisviewedasinformationretrievaltask:

Retrieve(recommend)allitemswhicharepredictedtobe"good"or

"relevant".

Commonprotocol:

Hidesomeitemswithknowngroundtruth

Rankitemsorpredictratings >Count >Crossvalidate

Groundtruth

established

by

human

domain

experts

Reality

ActuallyGood ActuallyBad

Predictio

n Rated

Good

TruePositive(tp) FalsePositive(fp)

Rated

Bad

FalseNegative (fn) True Negative(tn)

Metrics: Precision and Recall


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- 63 -


Metrics:PrecisionandRecall

Precision:ameasureofexactness,determinesthefractionofrelevant

itemsretrievedoutofallitemsretrieved

E.g.theproportionofrecommendedmoviesthatareactuallygood

Recall:ameasureofcompleteness,determinesthefractionofrelevant

itemsretrievedoutofallrelevantitems

E.g.theproportionofallgoodmoviesrecommended

Dilemma of IR measures in RS


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DilemmaofIRmeasuresinRS

IRmeasuresarefrequentlyapplied,however:

Groundtruthformostitemsactuallyunknown

What isarelevantitem?

Differentwaysofmeasuringprecisionpossible

Resultsfromofflineexperimentationmayhavelimitedpredictivepowerfor

onlineuserbehavior.

Metrics: Rank Score position matters


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- 65 -


RankScoreextendsrecallandprecisiontotakethepositionsofcorrect

itemsinarankedlistintoaccount

Particularly

important

in

recommender

systems

as

lower

ranked

items

may

be

overlookedbyusers

Learningtorank:Optimizemodelsforsuchmeasures(e.g.,AUC)

Metrics:RankScore positionmatters

Actually good

Item 237

Item 899

Recommended

(predicted as good)Item 345

Item 237

Item 187

Forauser:

hit

Accuracy measures


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- 66 -


Accuracymeasures

Datasetswithitemsratedbyusers

MovieLensdatasets100K10Mratings

Netflix100Mratings

Historicuser

ratings

constitute

ground

truth

Metricsmeasureerrorrate

MeanAbsoluteError(MAE)computesthedeviationbetween

predictedratingsandactualratings

RootMeanSquareError(RMSE)issimilartoMAE,butplaces

moreemphasisonlargerdeviation

Offline experimentation example


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- 67 -


Offlineexperimentationexample

Netflixcompetition

Webbasedmovierental

Prizeof$1,000,000foraccuracyimprovement(RMSE)of10%comparedtoown

Cinematch system.

Historicaldataset

~480Kusersrated~18Kmoviesonascaleof1to5(~100Mratings)

Last9ratings/userwithheld Probeset forteamsforevaluation

Quizset evaluatesteamssubmissionsforleaderboard

Testset usedbyNetflixtodeterminewinner

Today Rating predictiononlyseenasanadditionalinputintotherecommendationprocess

Animperfectworld


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- 68 -


Offlineevaluationisthecheapestvariant

Still,givesusvaluableinsights

andletsuscompareourresults(intheory)

Dangersandtrends:

Dominationofaccuracymeasures

Focusonsmallsetofdomains(40%onmoviesinCS)

Alternativeandcomplementarymeasures:

Diversity,Coverage,Novelty,Familiarity,Serendipity,Popularity,

Concentrationeffects(Longtail)

p

Onlineexperimentationexample


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- 69 -


p p

Effectivenessofdifferentalgorithmsfor

recommendingcellphonegames

[Jannach,Hegelich

09]

Involved150,000usersonacommercialmobile

internetportal

Comparisonof recommendermethods

Detailsandresults


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- 70 -


Recommendervariantsincluded:

Itembasedcollaborativefiltering

SlopeOne (alsocollaborativefiltering)

Contentbasedrecommendation

Hybridrecommendation

Toprateditems

Topsellers

Findings:

Personalizedmethodsincreasedsalesupto3.6%comparedtonon

personalized

Choiceofrecommendationalgorithmdependsonusersituation

(e.g.avoidcontentbasedRSinpostsalessituation)

}nonpersonalized

Nonexperimentalresearch


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- 71 -


Quasiexperiments

Lackrandomassignmentsofunitstodifferenttreatments

Nonexperimental/observationalresearch

Surveys/Questionnaires

Longitudinalresearch

Observationsoverlongperiodoftime

E.g.customerlifetimevalue,returningcustomers

Casestudies

Focusgroup

Interviews

Thinkaloudprotocols

Quasiexperimental


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- 72 -


SkiMatcher ResortFinderintroducedbySkiEurope.comtoprovideusers

withrecommendationsbasedontheirpreferences

ConversationalRS

questionandanswerdialog

matchingofuserpreferenceswithknowledgebase

DelgadoandDavidsonevaluatedthe

effectivenessoftherecommenderovera

4monthperiodin2001

Classifiedasaquasiexperiment

asusersdecideforthemselvesifthey

wanttousetherecommenderornot

SkiMatcherResults


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July August September October

UniqueVisitors 10,714 15,560 18,317 24,416

SkiMatcherUsers 1,027 1,673 1,878 2,558

NonSkiMatcher Users 9,687 13,887 16,439 21,858

RequestsforProposals 272 506 445 641

SkiMatcherUsers 75 143 161 229

NonSkiMatcher Users 197 363 284 412

Conversion 2.54% 3.25% 2.43% 2.63%

SkiMatcherUsers 7.30% 8.55% 8.57% 8.95%

NonSkiMatcher Users 2.03% 2.61% 1.73% 1.88%

IncreaseinConversion 359% 327% 496% 475%

[Delgado and Davidson, ENTER 2002]

InterpretingtheResults


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- 74 -


Thenatureofthisresearchdesignmeansthatquestionsofcausality

cannotbeanswered(lackofrandomassignments),suchas

Areusersoftherecommendersystemsmorelikelyconvert?

Doestherecommendersystemitselfcauseuserstoconvert?

SomehiddenexogenousvariablemightinfluencethechoiceofusingRSaswell

asconversion.

However,significant

correlation

between

using

the

recommender

systemandmakingarequestforaproposal

Sizeof

effect

has

been

replicated

in

other

domains

Tourism[Jannachetal.,JITT2009]

Electronicconsumerproducts

Observationalresearch


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- 75 -


Increaseddemandinniches/longtailproducts

Expostfromwebshopdata[Zankeretal.,ECWeb,2006]

Whatispopular? From:Jannachetal.,ProceedingsEC Web 2012


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- 76 -


Usercentricevaluation/Userstudies Increasedinterestinrecentyears

Variousnumbersofworkshops

ECWeb2012

Whatarethenexttopics?


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Twoadditionalmajorparadigmsofrecommendersystems

Contentbased

Knowledge

based

Hybridization:takethebestofdifferentparadigms

Advancedtopics:recommendersystemsareabouthumandecisionmaking


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- 78 -


Contentbasedrecommendation


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- 79 -


CollaborativefilteringdoesNOTrequireanyinformationabouttheitems,

However,itmightbereasonabletoexploitsuchinformation

E.g.recommendfantasynovelstopeoplewholikedfantasynovelsinthepast

Whatdoweneed:

Someinformationabouttheavailableitemssuchasthegenre("content")

Somesortofuserprofiledescribingwhattheuserlikes(thepreferences)

Thetask: Learnuserpreferences

Locate/recommenditemsthatare"similar"totheuserpreferences



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- 80 -


Contentbased:"Showmemoreofthe

samewhatI'veliked"

Whatisthe"content"?


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Thegenreisactuallynotpartofthecontentofabook

MostCBrecommendationmethodsoriginatefromInformationRetrieval

(IR)field:

Theitemdescriptionsareusuallyautomaticallyextracted(importantwords)

Goalistofindandrankinterestingtextdocuments(newsarticles,webpages)

Here:

ClassicalIRbasedmethodsbasedonkeywords

Noexpertrecommendationknowledgeinvolved

Userprofile(preferences)areratherlearnedthanexplicitlyelicited

Contentrepresentationanditemsimilarities


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Simpleapproach

Computethesimilarityofanunseenitemwiththeuserprofilebasedonthe

keywordoverlap(e.g.usingtheDicecoefficient)

sim(bi,bj)= |

|

| |

TermFrequencyInverseDocumentFrequency(TFIDF)


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Simplekeywordrepresentationhasitsproblems

Inparticularwhenautomaticallyextractedbecause

Noteverywordhassimilarimportance

Longerdocumentshaveahigherchancetohaveanoverlapwiththeuserprofile

Standardmeasure:TFIDF

Encodestextdocumentsasweightedtermvector

TF:Measures,howoftenatermappears(densityinadocument) Assumingthatimportanttermsappearmoreoften

Normalizationhastobedoneinordertotakedocumentlengthintoaccount

IDF:Aimstoreducetheweightoftermsthatappearinalldocuments

TFIDF


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Computetheoverallimportanceofkeywords

Givenakeywordiandadocumentj

TFIDF i,j TFi,j * IDFi

Termfrequency(TF)

Letfreqi,jnumberofoccurrencesofkeywordiindocumentj

LetmaxOthersi,jdenotethehighestnumberofoccurrencesofanother

keywordofj

, ,

,

InverseDocumentFrequency(IDF)

N:numberofallrecommendabledocuments

n(i):numberofdocumentsinwhichkeywordiappears

ExampleTFIDFrepresentation


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- 85 -


Figure taken from http://informationretrieval.org

Moreonthevectorspacemodel


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Vectorsareusuallylongandsparse

Improvements

Removestopwords("a","the",..) Usestemming

Sizecutoffs(onlyusetopnmostrepresentativewords,e.g.around100)

Useadditionalknowledge,usemoreelaboratemethodsforfeatureselection

Detectionofphrasesasterms(suchasUnitedNations)

Limitations

Semanticmeaningremainsunknown

Example:usageofawordinanegativecontext

"thereisnothingonthemenuthatavegetarianwouldlike.."

Usualsimilaritymetrictocomparevectors:Cosinesimilarity(angle)

Recommendingitems


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Simplemethod:nearestneighbors

GivenasetofdocumentsDalreadyratedbytheuser(like/dislike)

FindthennearestneighborsofanotyetseenitemiinD

Taketheseratingstopredictarating/votefori

(Variations:neighborhoodsize,lower/uppersimilaritythresholds)

Querybasedretrieval:Rocchio's method

TheSMARTSystem:Usersareallowedtorate(relevant/irrelevant)retrieveddocuments(feedback)

Thesystemthenlearnsaprototypeofrelevant/irrelevantdocuments

Queriesarethenautomaticallyextendedwithadditionalterms/weightof

relevantdocuments

Rocchio details


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DocumentcollectionsD+ andD

, , usedtofinetune

thefeedback

oftenonlypositivefeedback

isused

Probabilisticmethods


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Recommendationasclassicaltextclassificationproblem

Longhistoryofusingprobabilisticmethods

Simpleapproach:

2classes:like/dislike

SimpleBooleandocumentrepresentation

Calculateprobabilitythatdocumentisliked/dislikedbasedonBayestheorem

Remember:P(Label=1|X)=k*P(X|Label=1) * P(Label=1)

Improvements


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Sidenote:Conditionalindependenceofeventsdoesinfactnothold

New/YorkandHong/Kong"

Still,goodaccuracycanbeachieved

Booleanrepresentationsimplistic

Keywordcountslost

Moreelaborateprobabilisticmethods

E.g.estimateprobabilityoftermvoccurringinadocumentofclassCby

relativefrequencyofvinalldocumentsoftheclass

Otherlinearclassificationalgorithms(machinelearning)canbeused

SupportVectorMachines,..

Limitationsofcontentbasedrecommendationmethods


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Keywordsalonemaynotbesufficienttojudgequality/relevanceofa

documentorwebpage

Uptodateness,usability,aesthetics,writingstyle

Contentmayalsobelimited/tooshort

Contentmaynotbeautomaticallyextractable(multimedia)

Rampupphaserequired

Some

training

data

is

still

required Web2.0:Useothersourcestolearntheuserpreferences

Overspecialization

Algorithmstendtopropose"moreofthesame"

E.g.toosimilarnewsitems


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Whydoweneedknowledgebasedrecommendation?


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Productswithlownumberofavailableratings

Timespanplaysanimportantrole

Fiveyearoldratingsforcomputers

Userlifestyleorfamilysituationchanges

Customerswant

to

define

their

requirements

explicitly

Thecolorofthecarshouldbeblack"

Knowledgebasedrecommendation


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Knowledgebased:"Tellmewhatfits

basedonmyneeds"

KnowledgebasedrecommendationI


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Explicitdomainknowledge

Salesknowledgeelicitationfromdomainexperts

Systemmimicsthebehaviorofexperiencedsalesassistant

Bestpracticesalesinteractions

Canguaranteecorrectrecommendations(determinism)withrespectto

expertknowledge

Conversationalinteractionstrategy

Opposedtooneshotinteraction

Elicitationofuserrequirements

Transfer

of

product

knowledge

(educating

users)

KnowledgeBasedRecommendationII


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Differentviewsonknowledge

Similarityfunctions Determinematchingdegreebetweenqueryanditem(casebasedRS)

UtilitybasedRS

E.g.MAUT Multiattributeutilitytheory

Logicbasedknowledgedescriptions(fromdomainexpert)

E.g.Hardandsoftconstraints

ConstraintbasedrecommendationI


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AknowledgebasedRSformulatedasconstraintsatisfactionproblem

Def.

XI,XU:VariablesdescribingitemsandusermodelwithdomainD

(e.g.lowerfocallength,purpose)

KB:Knowledgebase comprisingconstraints and domainrestrictions

(e.g.IFpurpose=on

travel

THENlowerfocallength


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MultiAttributeUtilityTheory(MAUT)

Eachitemisevaluatedaccordingtoapredefinedsetofdimensionsthatprovide

anaggregatedviewonthebasicitemproperties

E.g.quality andeconomy aredimensionsinthedomainofdigitalcameras

id value quality economy

price 250>250

510

105

mpix 8

>8

4

10

10

6

optzoom 9

>9

6

10

9

6

... ... ... ...

CustomerspecificitemutilitieswithMAUT

Customer interests: customer quality economy


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Customerinterests:

Itemutilities:

customer quality economy

Cu1 80% 20%

Cu2 40% 60%

quality economy utility:cu1 utility:cu2

P1 (5,4,6,6,3,7,10) = 41 (10,10,9,10,10,10,6) = 65 45.8 [8] 55.4 [6]

P2 (5,4,6,6,10,10,8) = 49 (10,10,9,10,7,8,10) = 64 52.0 [7] 58.0 [1]

P3 (5,4,10,6,10,10,8) = 53 (10,10,6,10,7,8,10) = 61 54.6 [5] 57.8 [2]

... ... ... ...

*

**

* **

ConstraintbasedrecommendationII

BUT Wh if l i i ?


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BUT:Whatifnosolutionexists?

notsatisfiable debuggingofknowledgebase

notsatisfiable but

correct debuggingofuserrequirements

Applicationofmodelbaseddiagnosisfordebugginguser

requirements

Diagnoses: issatisfiable

Repairs: issatisfiable

Conflictsets: notsatisfiable

IKBSRS

IKB

IKBSRS )\(

IKBCSSRSCS :

IKBSRS repair )\(

IKB

Example:findminimalrelaxations(minimaldiagnoses)

KnowledgeBase: Productcatalogue:


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Usermodel(SRS)

R1 Motives Landscape

R2 Brandpreference Canon

R3 Max.cost 350EUR

PowershotXY

Brand Canon

Lowerfocallength 35

Upperfocallength 140

Price 420 EUR

LumixBrand Panasonic

Lowerfocallength 28

Upperfocallength 112

Price 319 EUR

LHS RHS

C1 TRUE Brand =Brandpref.

C2 Motives=Landscape Low.foc.Length=


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Computationofminimalrevisionsofrequirements

Doyouwanttorelaxyourbrandpreference?

AcceptPanasonicinsteadofCanonbrand

Orisphotographinglandscapeswithawideanglelensandmaximumcostless

important?

Lowerfocallength>28mmandPrice>350EUR

Optionallyguidedbysomepredefinedweightsorpast communitybehavior

Beawareofpossiblerevisions(e.g.age,familystatus,)

ConstraintbasedrecommendationIII


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Morevariantsofrecommendationtask

Customersmaybenotknowwhattheyareseeking

Find"diverse"setsofitems

Notionofsimilarity/dissimilarity

Ideathatusersnavigateaproductspace

Ifrecommendationsaremorediversethanuserscannavigateviacritiqueson

recommended"entrypoints"moreefficiently(lessstepsofinteraction)

Bundlingofrecommendations

Finditembundlesthatmatchtogetheraccordingtosomeknowledge

E.g.travelpackages,skincaretreatmentsorfinancialportfolios

RSfordifferentitemcategories,CSPrestrictsconfiguring ofbundles

Conversationalstrategies

Process consisting of multiple


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Processconsistingofmultiple

conversationalmoves

Resemblesnaturalsalesinteractions

Notalluserrequirementsknownbeforehand Customersarerarelysatisfiedwiththeinitial

recommendations

Differentstylesofpreferenceelicitation:

Freetextqueryinterface Askingtechnical/genericproperties

Images/inspiration

ProposingandCritiquing

Example:adaptivestrategyselection


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Statemodel,differentactionspossible

Proposeitem,askuser,relax/tightenresultset,

[Riccietal.,JITT,2009]

Limitationsofknowledgebasedrecommendationmethods

Cost of knowledge acquisition


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Costofknowledgeacquisition

Fromdomainexperts

Fromusers

Remedy:

exploit

web

resources

Accuracyofpreferencemodels

Veryfinegranularpreferencemodelsrequiremanyinteractioncycleswiththe

userorsufficientdetaileddataabouttheuser Remedy:usecollaborativefiltering,estimatesthepreferenceofauser

However:preferencemodelsmaybeinstable

E.g.asymmetricdominanceeffectsanddecoyitems


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Hybridrecommendersystems


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Allthreebasetechniquesarenaturallyincorporatedbyagoodsalesassistance

(atdifferentstagesofthesalesact)buthavetheirshortcomings

Ideaofcrossingtwo(ormore)species/implementations

hybrida[lat.]:denotesanobjectmadebycombiningtwodifferentelements

Avoidsomeoftheshortcomings

Reachdesirablepropertiesnotpresentinindividualapproaches

Differenthybridizationdesigns

Monolithicexploitingdifferentfeatures

Parallel

use

of

several

systems Pipelinedinvocationofdifferentsystems

Monolithichybridizationdesign


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Onlyasinglerecommendationcomponent

Hybridizationis"virtual"inthesensethat

Features/knowledgesourcesofdifferentparadigmsarecombined

Monolithichybridizationdesigns:Featurecombination


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"Hybrid"userfeatures:

Socialfeatures:Movieslikedbyuser Contentfeatures:Comedieslikedbyuser,dramaslikedbyuser

Hybridfeatures:userswholikemanymoviesthatarecomedies,

the

common

knowledge

engineering

effort

that

involves

inventing

good

featurestoenablesuccessfullearning[BHC98]

Monolithichybridizationdesigns:Featureaugmentation

C t t b t d ll b ti filt i [MMN02]


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Contentboostedcollaborativefiltering[MMN02]

Basedoncontentfeaturesadditionalratingsarecreated

E.g.AlicelikesItems1and3(unaryratings)

Item7issimilarto1and3byadegreeof0,75

ThusAlicelikesItem7by0,75

Itemmatricesbecomelesssparse

Recommendationofresearchpapers[TMA+04]

Citationsinterpretedascollaborativerecommendations

Integratedincontentbasedrecommendationmethod

Parallelizedhybridizationdesign

Outputofseveralexistingimplementationscombined


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Leastinvasivedesign

Weightingorvotingschemeapplied

Weightscanbelearneddynamically

Parallelizedhybridizationdesign:Switching

Specialcaseofdynamicweights(allweightsexceptoneare0)


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Requiresanoraclethatdecideswhichrecommenderisused

Example:

Switchingisbasedonsomequalitycriteria:

E.g.

if

too

few

ratings

in

the

system,

use

knowledge

based,

else

collaborative

Pipelinedhybridizationdesigns

One recommender system pre processes some input for the subsequent


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Onerecommendersystempreprocessessomeinputforthesubsequent

one

Cascade

Metalevel

Refinementofrecommendationlists(cascade)

Learningofmodel(e.g.collaborativeknowledgebasedmetalevel)

Pipelinedhybridizationdesigns:Cascade

Item1 0.8 2

Recommender 2

Item1 0.5 1

Recommender 1


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Recommendationlistiscontinuallyreduced

Firstrecommenderexcludesitems

Removeabsolutenogoitems(e.g.knowledgebased)

Secondrecommenderassignsscore

Orderingandrefinement(e.g.collaborative)

Item2 0.9 1

Item3 0.4 3

Item4 0

Item5 0

Item2 0

Item3 0.3 2

Item4 0.1 3

Item5 0

Item1 0,80 1

Item2 0,00

Item3 0,40 2

Item4 0,00Item5 0,00

Recommender cascaded (rec1, rec2)

Pipelinedhybridizationdesigns:Metalevel

Successorexploitsamodel builtbypredecessor


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ismodelbuiltbyRSn1exploitedbyRSn

Examples:

Fabsystem:contentbased,collaborativerecommendation[BS97]

Online

news

domain Contendbasedrecommenderbuildsusermodelsbasedonweightedtermvectors

Collaborative filteringidentifiessimilarpeersbasedonweightedtermvectorsbutmakesrecommendationsbasedonratings

Collaborative,constraintbasedmetalevelRS

Collaborativefilteringidentifiessimilarpeers Aconstraintbaseislearnedbyexploitingthebehaviorofsimilarpeers

Learnedconstraintsareemployedtocomputerecommendations

),,(),(1

nrecnlevelmeta iureciurec

Whatisthebesthybridizationstrategy?

Only few works that compare strategies from the metaperspective


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Onlyfewworksthatcomparestrategiesfromthemeta perspective

Forinstance,[Burke02]

Mostdatasetsdonotallowtocomparedifferentrecommendationparadigms

I.e.ratings,requirements,itemfeatures,domainknowledge,critiquesrarely

availableinasingledataset

Someconclusionsaresupportedbyempiricalfindings

Monolithic:preprocessingefforttradedinformoreknowledgeincluded

Parallel:requirescarefuldesignofscoresfromdifferentpredictors Pipelined:workswellfortwoantitheticapproaches

Netflixcompetition stackingrecommendersystems

Weighteddesignbasedon>100predictors recommendationfunctions

Adaptiveswitchingofweightsbasedonusermodel,parameters

AdvancedtopicsI


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Explanationsinrecommendersystems

Motivation


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ThedigitalcameraProfishotisamustbuyforyoubecause....

Whyshouldrecommendersystemsdealwithexplanationsatall?

Theanswerisrelatedtothetwopartiesprovidingandreceiving

recommendations: Asellingagentmaybeinterestedinpromotingparticularproducts

Abuyingagentisconcernedaboutmakingtherightbuyingdecision

Explanationsinrecommendersystems

Additional information to explain the systems output following some


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Additionalinformationtoexplainthesystem soutputfollowingsome

objectives

Objectivesofexplanations


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Transparency

Validity

Trustworthiness

Persuasiveness

Effectiveness

Efficiency

Satisfaction

Relevance

Comprehensibility

Education

Explanationsingeneral

How?andWhy?explanationsinexpertsystems


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Formofabductive reasoning

Given: (itemiisrecommendedbymethodRS)

Find s.t.

Principleofsuccinctness

Findsmallestsubsetof s.t.

i.e.forall

holds

Butadditionalfiltering

Somepartsrelevantfor

deduction,mightbeobvious

forhumans

[Friedrich&Zanker,AIMagazine,2011]

TaxonomyforgeneratingexplanationsinRS

Majordesigndimensionsofcurrentexplanationcomponents:

Category of reasoning model for generating explanations


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Categoryofreasoningmodelforgeneratingexplanations

Whitebox

Blackbox

RSparadigmforgeneratingexplanations

Determinestheexploitablesemanticrelations

Informationcategories

RSparadigmsandtheirontologies

Classesofobjects

U


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Users

Items

Properties

Nary relationsbetweenthem

Collaborativefiltering

NeighborhoodbasedCF(a)

Matrixfactorization(b)

Introducesadditionalfactorsasproxiesfor

determining

similarities

RSparadigmsandtheirontologies

Contentbased

Properties characterizing items


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Propertiescharacterizingitems

TF*IDFmodel

Knowledgebased

Propertiesofitems

Properties

of

user

model Additionalmediatingdomainconcepts

Similaritybetween

items


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Similaritybetween

users

Tags

Tagrelevance(foritem)

Tag

preference

(of

user)

Thermencheck.com(hotspringresorts)


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The water has favorable properties for X, butit is unknown if it also cures Y.

It offers organic food, but no kosher food.

It offers services for families with smallchildren, such as X, Y and Z.

It is a spa resort of medium size offering

around 1000 beds.

Resultsfromtestingtheexplanationfeature

PerceivedPositive

Usage exp+**


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Knowledgeableexplanationssignificantlyincreasetheusers

perceivedutility

Perceivedutility

strongly

correlates

with

usage

intention

etc.

Explanation

Trust

Perceived

UtilityUsageexp.

Recommend

others

Intentionto

repeated

usage**

sign.

Recommender systems tutorial.pdf

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